e15526 Background: Practice guidelines recommend using P to treat K-Ras WT mCRC patients where it was shown to significantly extend overall survival (OS). Still, a proportion of patients will not achieve this goal. We propose a simplified predictive score to identify patients who are likely to benefit from P treatment. Methods: NCT00364013 was used as training dataset (TRS) (n = 460) with NCT00339183 (TES1) (n = 479) and NCT00113763 (TES2) (n = 191) as validations sets. Datasets were obtained from www.projectdatasphere.org and included K-Ras WT mCRC patients treated with P in combination or not with FOLFOX4 (FOL) or FOLFIRI as 1st, 2nd, or 3rd line therapy. TRS was used to generate synthetic representations (SRs) for each patient through the integration of 36 clinical and analytical features collected, respectively, during the screening phase and the first month of inclusion. These SRs were then input into a deep learning framework (DLF) to identify subgroup of patients based on their similarities. The resultant subpopulations were correlated with OS. Differential variables between subgroups were identified through feature contribution analysis and included in a multivariable logistic regression model. Independent predictive factors found to be statistically significant were used to generate a predictive score of P response at baseline that was validated in the test sets. Results: DLF identified two different subpopulations: SPA (n = 162) and SPB (n = 298). Patients in SPA had a lower risk of death when treated with P/FOL compared to FOL (HR 0.68 95%CI 0.48-0.99; p = 0.04). Patients in SPB showed no significant differences between P/FOL and FOL (p = 0.27). Feature contribution analysis identified 15 differential features between both subpopulations. From these, CEA > 174 ng/ml, ALP > 131 IU, LDH > 703 IU, and platelets > 374 109/L were selected to create a simplified predictive score for P response ranging 0-18 (if > than the depicted values: 6.5 points for CEA, 5.5 for LDH, and 3 points for each other characteristic). When applied to TRS, this score yielded an area under the curve of 0.87 (95%CI: 0.84-0.91). A score ≥8.5 was positively correlated to a longer OS after P/FOL compared to FOL (HR 0.65 95%CI 0.43-0.98; p = .04). No significant differences were observed between P/FOL and FOL in patients with a score < 8.5 (p = 0.89). The predictive score was then validated in the two test sets with similar results (score ≥8.5, TES1: HR 0.59 95%CI 0.40-0.88 p = .009; TES2: HR: HR 0.58 95%CI 0.35-0.96 p = .03; score < 8.5, TES1: p = .5; TES2: p = .1). Conclusions: Based on CEA, ALP, LDH and platelet baseline levels, this easily applicable predictive score might be helpful to accurately select K-Ras WT mCRC patients who would benefit from P treatment. Further work is required to validate this approach in prospective cohorts of patients.

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e17032 Background: The optimal time to begin therapy in mCRPC patients who have either no, or minimal symptoms is not defined yet. Estimating prognosis of these patients can avoid undertreatment or overtreatment and guide the follow-up intensity. This study has investigated the ability of a deep learning framework (DLF) to identify asymptomatic or mildly symptomatic mCRPC patients with high risk of disease progression and mortality using PSA and testosterone levels. Methods: Data from the control arm of the NCT00554229 trial was obtained from www.projectdatasphere.org . These mCRPC patients were treated with placebo alone. The dataset consisted of more than 150 clinical variables. We generated synthetic fingerprints (SF) for each patient through the integration of PSA values collected at baseline and during the first 90 days of follow-up and testosterone levels at baseline. These SF were then input into a DLF to identify subgroup of patients based on their similarities. The resultant subgroups were correlated with progression-free survival (PFS) and overall survival (OS). Feature contribution analysis identified the significant predictive clinical variables in each subgroup. Results: After discarding missing data, 189 patients were eligible for this exploratory study. Median PFS and OS for the total population were 8.7 months and 24.5 months, respectively. DLF identified two different subpopulations, SPA (n = 89) and SPB (n = 100), with significantly different survival outcome. Patients in SPA had a lower risk of progression (median PFS 12.2 months vs. 7.2 months; hazard ratio 0.54, 95% CI 0.41-0.71; p < .0001) and death (median OS not reached vs. 20.3 months; hazard ratio 0.33, 95% CI 0.21-0.52; p < .0001) compared to patients in SPB. SPA signature was significantly correlated to leuprolide treatment. These patients also had higher levels of haemoglobin, red blood cells, haematocrit, total bilirubin, Ca, phosphate, and K. Instead, SPB signature was significantly correlated with presence of > 3 bone metastasis, lack of energy or forced time to bed in the FACT-P and moderate problems in selfcare in the EQ-5D, and treatment with goserelin, tamsulosin, opioids and contact laxatives. In addition, these patients had higher levels of platelets, basophils, monocytes, ALP, AST, LDH, PSA, free PSA, Mg, Na, and specific gravity. Conclusions: DLF identifies mCRPC subtypes that correlate with higher risk of progression and death according to PSA and testosterone levels. Model predictions also suggest complex interactions among many clinical features that might be important modulators of key clinical traits and outcome. Further work is required to validate this approach to anticipate the clinical course of asymptomatic or mildly symptomatic mCRPC patients and help to better decide when to start appropriate therapy.

e13550 Background: Treatment with DP improves survival in mCRCP but is associated with significant toxicity. The question remains as to whether the improved survival is worth the toxicity risk. In this study, we have investigated the ability of a DLF to identify those patients on which treatment with DP is likely to be beneficial. Methods: The dataset (n = 2028) included a compilation of records from 4 randomized phase 3 trials (NCT00273338, NCT00988208, NCT00617669, and NCT00519285) in which the comparator arm consisted of D (75 mg/m2 q3w) plus P (5mg PO bid) as first-line therapy for mCRPC patients. Data was obtained from www.projectdatasphere.org and contained more than 150 clinical variables at baseline. These were used to generate synthetic state representations (SSR) of every patient that were then input into the DLF to identify subgroup of patients based on their similarities. The resultant subgroups were correlated with progression-free survival (PFS) and overall survival (OS). Results: DLF identified three patient subgroups with specific clinical traits: LL (n = 438), HL (n = 386) and HH (n = 1204). These subpopulations varied in clinical outcome after DP treatment. LL patients (median PFS 19.3 months) had a lower risk of progression compared to HL (median PFS 8.2 months; HR 0.32, 95% CI 0.26-0.40, p < .0001) and HH (median PFS: 9.2 months; HR 0.44, 95% CI 0.36-0.52, p < .0001). No differences were observed for HL and HH. In reference to OS, patients in LL (median OS not reached) and HL (median OS 27 .2 months) did not show any difference; however, both subpopulations showed a lower risk of death compared to HH (median OS 17.7 months) (HR 0.45, 95% CI 0.38-0.54, p < 0.001; HR 0.55 95% CI 0.46-0.62, p < 0.001, respectively). Feature contribution analysis showed that LL signature was associated with ECOG0 and lower levels of PSA, LDH, ALP and AST. LL patients had received less cancer therapy since diagnosis and were more treated with biguanides. In contrast, HL signature had more patients with ECOG 1, and intermediate levels of PSA, LDH, ALP and AST. HL patients had received more hormonal therapy since diagnosis and were more treated with HMG-COA reductase inhibitors. Finally, HH signature was characterised with the highest levels of PSA, LDH, ALP and AST and were more treated with opioids. Other major differences were observed on anthropometrics, vital signs, testosterone, albumin and ALT levels, and non-opioid concomitant medications. Conclusions: We show a methodology that identifies distinct baseline clinical features correlated to the risk of progression and death after DP treatment. While evident for LL (PFS and OS) and HL (OS), HH patients would not receive any treatment benefit on survival. Further work is required to validate this approach as a novel predictive tool for DP treatment decision making on mCRPC patients.